An Analysis of Monopolistic and Competitive Take‐Back Schemes for WEEE Recycling

We study two prevailing types of take‐back schemes for electrical and electronic equipment waste recycling: monopolistic and competitive. We address key market and operating factors that make one scheme preferable to the other from the viewpoints of recyclers, manufacturers, and consumers. To this end, we model competitive decision making in both take‐back schemes as two‐stage sequential games between competing manufacturers and recyclers. Deriving and computing equilibria, we find that the competitive take‐back scheme often accomplishes a win–win situation, that is, lower product prices, and higher recycler and manufacturer profits. Exceptionally, recyclers prefer the monopolistic scheme when the substitutability level between the manufacturers' original products is high or economies of scale in recycling are very strong. We show that consolidation of the recycling industry could benefit all stakeholders when the economies of scale in recycling are strong, provided that manufacturer's products are not highly substitutable. Higher collection rates also render recycler consolidation desirable for all stakeholders. We also identify a potential free rider problem in the monopolistic scheme when recyclers differ in operational efficiency, and propose mechanisms to eliminate the discrepancy. We show that our results and insights are robust to the degree of competition within the recycling industry.     

Pricing and Logistics Decisions for a Private‐Sector Provider in the Cash Supply Chain

We study a problem faced by a secure‐logistics provider (SLP) of maximizing profit by jointly pricing the services of fit‐sorting and transporting cash along with the design of the supporting logistics network, in a market consisting of a population of Depository Institutions (DIs). The need to jointly price the services assumes significance because they are partial substitutes of one another. Our study finds that the influence of the logistics network on prices is especially strong when there are non‐linearities in the cost of provisioning the logistics services. Furthermore, the impact of logistics decisions on different types of pricing schemes (e.g., volume discount, bundled pricing) is different, both in its structure and extent. In present times, when the market for the fit‐sorting service is relatively immature, our findings have major implications to the way an SLP's business is managed.     

Analyzing the Integration of WiMAX and Wi‐Fi Services: Bandwidth Sharing and Channel Collaboration

Several emerging studies have focused on the pricing issue of bandwidth sharing between Wi‐Fi and WiMAX networks; however, most either concentrate on the design of collaborated protocols or figure out the issue without the overall consideration of consumer preferences and contract design. In this study, we explore a wireless service market in which there are two wireless service providers operating Wi‐Fi and WiMAX. One of the research dimensions given in this study is whether wireless service providers implement bandwidth sharing, while the other is whether they make decisions individually or jointly. By involving consumer preferences and a wholesale price contract in the present model, we find that bandwidth sharing would benefit a WiMAX service provider, yet a Wi‐Fi service provider would make no significant savings under a wholesale price contract. In addition, the profit of a WiMAX service provider may increase with Wi‐Fi coverage when bandwidth sharing has been implemented but decrease with Wi‐Fi coverage when both wireless services operate without bandwidth sharing. Furthermore, the WiMAX service provider allocates more capacity when the average usage rate increases, but lowers the expenditure of capacity when the average usage rate is too high.     

Pricing and Operational Performance in Discretionary Services

In many services, for example, website or landscape design, the value or quality derived by a customer depends upon the service time, and this valuation differs across customers. Customers procure the service based on the expected value to be delivered, prices charged, and the timeliness of service. We investigate the performance of the optimal pricing scheme as well as two commonly used pricing schemes (fixed fee and time‐based pricing) for such services on important dimensions such as revenue, demand served, and utilization. We propose a novel model that captures the above features and wherein both service rate and demand are endogenous and functions of the pricing scheme. In particular, service time is an outcome of the pricing scheme adopted and the heterogeneous valuations of customers, unlike in the queueing‐based pricing literature. We find that the service system may benefit from a greater variance in consumer valuations, and the performance of pricing schemes is impacted by the shape of the distribution of customers' valuation of service time and the responsiveness desired by customers. Both the fixed fee and time‐based schemes do well relative to the optimal pricing scheme in terms of revenue in many plausible scenarios, but there are substantial differences between the pricing schemes in some important operational metrics. For instance, the fixed fee scheme serves more customers and has higher utilization than the time‐based scheme. We also explore variants of the fixed and time‐based schemes that have better revenue performance and show that the two‐part tariff which is a combination of fixed and time‐based pricing can do as well as the optimal scheme in terms of revenue.     

Optimal Pricing Strategies for Capacity Leasing Based on Time and Volume Usage in Telecommunication Networks

In this study, we examined optimal pricing strategies for “pay‐per‐time,” “pay‐per‐volume,” and “pay‐per‐both‐time‐and‐volume” based leasing of data networks in a monopoly environment. Conventionally, network capacity distribution includes short‐/long‐term bandwidth and/or usage time leasing. When customers choose connection‐time–based pricing, their rational behavior is to fully utilize the bandwidth capacity within a fixed time period, which may cause the network to burst (or overload). Conversely, when customers choose volume‐based strategies their rational behavior is to send only the minimum bytes necessary (even for time‐fixed tasks for real time applications), causing the quality of the task to decrease, which in turn creates an opportunity cost for the provider. Choosing a pay‐per time and volume hybridized pricing scheme allows customers to take advantage of both pricing strategies while lessening the disadvantages of each, because consumers generally have both time‐ and size‐fixed tasks such as batch data transactions. One of the key contributions of this study is to show that pay‐per both time and volume pricing is a viable and often preferable alternative to the offerings based on only time or volume, and that judicious use of such a pricing policy is profitable to the network provider.     

Quality‐Speed Competition in Customer‐Intensive Services with Boundedly Rational Customers

We consider a system in which two competing servers provide customer‐intensive services and the service reward is affected by the length of service time. The customers are boundedly rational and choose their service providers according to a logit model. We demonstrate that the service provider revenue function is unimodal in the service rate, its decision variable, and show that the service rate competition has a unique and stable equilibrium. We then study the price decision under three scenarios with the price determined by a revenue‐maximizing firm, a welfare‐maximizing social planner, or two servers in competition. We find that the socially optimal price, subject to the requirement that the customer actual utility must be non‐negative, is always lower than the competition equilibrium price which, in turn, is lower than the revenue‐maximizing monopoly price. However, if the customer actual utility is allowed to be negative in social optimization, the socially optimal price can be higher than the other two prices in a large market.     

Competitive Pricing in a Multi‐Product Multi‐Attribute Environment

We address the problem of simultaneous pricing of a line of several products, both complementary products and substitutes, with a number of distinct price differentiation classes for each product (e.g., volume discounts, different distribution channels, and customer segments) in both monopolistic and oligopolistic settings. We provide a generic framework to tackle this problem, consider several families of demand models, and focus on a real‐world case‐study example. We propose an iterative relaxation algorithm, and state sufficient conditions for convergence of the algorithm. Using historical sales and price data from a retailer, we apply our solution algorithm to suggest optimal pricing, and report on numerical results.     

Capacity Planning and Allocation for Web‐Based Applications

Motivated by the technology division of a financial services firm, we study the problem of capacity planning and allocation for Web‐based applications. The steady growth in Web traffic has affected the quality of service (QoS) as measured by response time (RT), for numerous e‐businesses. In addition, the lack of understanding of system interactions and availability of proper planning tools has impeded effective capacity management. Managers typically make decisions to add server capacity on an ad hoc basis when systems reach critical response levels. Very often this turns out to be too late and results in extremely long response times and the system crashes. We present an analytical model to understand system interactions with the goal of making better server capacity decisions based on the results. The model studies the relationships and important interactions between the various components of a Web‐based application using a continuous time Markov chain embedded in a queuing network as the basic framework. We use several structured aggregation schemes to appropriately represent a complex system, and demonstrate how the model can be used to quickly predict system performance, which facilitates effective capacity allocation decision making. Using simulation as a benchmark, we show that our model produces results within 5% accuracy at a fraction of the time of simulation, even at high traffic intensities. This knowledge helps managers quickly analyze the performance of the system and better plan server capacity to maintain desirable levels of QoS. We also demonstrate how to utilize a combination of dedicated and shared resources to achieve QoS using fewer servers.     

A Comparison of Product Take‐Back Compliance Schemes

Product take‐back regulation, under which firms finance the collection and treatment of their end‐of‐life products, is a widely used environmental program. One of the most common compliance schemes is collectively with cost allocation by market share. As an alternative, individual compliance scheme is considered. Assuming that firms can choose their compliance scheme, we compare these two schemes with respect to the costs they impose on firms and environmental benefits. We show that high collection targets and large market shares among firms in a collective compliance scheme make it more cost‐effective. From an environmental benefits perspective, the prevailing intuition is that collection rates will be higher under collective schemes but individual compliance will provide more incentive for higher recyclability levels. Our results challenge both of these premises. We identify conditions under which collection rates are higher when firms comply individually and recyclability levels are higher when firms comply collectively and allocate costs with respect to market shares.     

Newsvendor Pricing Problem in a Two‐Sided Market

We study the pricing problem of a “platform” intermediary to jointly determine the selling price of the platforms (hardware) sold to consumers and the royalty charged to content developers for content (software), when the demands for content and for platforms are interdependent. Our model elucidates the impact of supply chain replenishment costs and demand uncertainty on the strategic issues of platform pricing in a two‐sided market.     

Profit Sharing and the Firm‐Size Wage Premium

This study analyzes the relationships among wages, firm size, and profit sharing schemes. We develop a simple theoretical model and explore the relationship empirically using high‐quality panel data. The theoretical model shows that the firm‐size wage premium decreases in the presence of profit sharing. The empirical results based on rich matched employee‐employer data for private sector wage earners in Finland show that the firm‐size wage premium is modest, and it becomes negligible when we account for profit sharing and covariates describing assortative matching and monopsony behavior. The analysis suggests that profit sharing schemes embody effects of firm‐specific unobservables that raise productivity, support rent sharing, and boost wages.     

A Mobile Platform's In‐App Advertising Contract Under Agency Pricing for App Sales

Unlike advertising in traditional media, a mobile platform's in‐app advertising market exhibits two unique features—split structure of the mobile platform with a platform owner and an app developer jointly provisioning in‐app advertising, and agency pricing for app sales. We develop a two‐sided market model to analyze the role of these two unique features in determining the platform owner's optimal advertising revenue‐sharing contract. Our results reveal an interesting N‐shaped dynamic regarding the platform owner's optimal choice of her ad revenue share with respect to the overall advertisers’ valuation of in‐app ads. We identify a between‐agent subsidization strategy for the platform owner, where she finds it optimal to subsidize the developer via the advertising channel, leading to greater profits for both of them. We find that the advertising revenue‐sharing contract under agency pricing for app sales leads to a higher app price than would be offered by the integrated platform found in traditional advertising. However, the ad price is coordinated under the platform owner's optimal choice of ad revenue share when she obtains revenue from both the advertising and app sales channels, leading to an alignment of her interest with the app developer's on ad level.     

A Note on the Relationship Among Capacity,Pricing, and Inventory in a Make‐to‐Stock System

We address the simultaneous determination of pricing, production, and capacity investment decisions by a monopolistic firm in a multi‐period setting under demand uncertainty. We analyze the optimal decision with particular emphasis on the relationship between price and capacity. We consider models that allow for either bi‐directional price changes or models with markdowns only, and in the latter case we prove that capacity and price are strategic substitutes.     

The Impact of E‐Replenishment Strategy on Make‐to‐Order Supply Chain Performance

This research investigates the impact of electronic replenishment strategy on the operational activities and performance of a two‐stage make‐to‐order supply chain. We develop simulation‐based rolling schedule procedures that link the replenishment processes of the channel members and apply them in an experimental analysis to study manual, semi‐automated, and fully automated e‐replenishment strategies in decentralized and coordinated decision‐making supply chain structures. The average operational cost reductions for moving from a manual‐based system to a fully automated system are 19.6, 29.5, and 12.5%, respectively, for traditional decentralized, decentralized with information sharing, and coordinated supply chain structures. The savings are neither equally distributed among participants, nor consistent across supply chain structures. As expected, for the fully coordinated system, total costs monotonically decrease with higher levels of automation. However, for the two decentralized structures, under which most firms operate today, counter‐intuitive findings reveal that the unilateral application of e‐procurement technology by the buyer may lower his purchasing costs, but increase the seller's and system's costs. The exact nature of the relationship is determined by the channel's operational flexibility. Broader results indicate that while the potential economic benefit of e‐replenishment in a decentralized system is substantial, greater operational improvements maybe possible through supply chain coordination.     

Revenue‐Sharing vs. Wholesale‐Price Contracts in Assembly Systems with Random Demand

Assembly and kitting operations, as well as jointly sold products, are rather basic yet intriguing A decentralized supply chains, where achieving coordination through appropriate incentives is very important, especially when demand is uncertain. We investigate two very distinct types of arrangements between an assembler/retailer and its suppliers. One scheme is a vendor‐managed inventory with revenue sharing, and the other a wholesale‐price driven contract. In the VMI case, each supplier faces strategic uncertainty as to the amounts of components, which need to be mated with its own, that other suppliers will deliver. We explore the resulting components' delivery quantities equilibrium in this decentralized supply chain and its implications for participants' and system's expected profits. We derive the revenue shares the assembler should select in order to maximize its own profits. We then explore a revenue‐plus‐surplus‐subsidy incentive scheme, where, in addition to a share of revenue, the assembler also provides a subsidy to component suppliers for their unsold components. We show that, by using this two‐parameter contract, the assembler can achieve channel coordination and increase the profits of all parties involved. We then explore a wholesale‐price‐driven scheme, both as a single lever and in combination with buybacks. The channel performance of a wholesale‐price‐only scheme is shown to degrade with the number of suppliers, which is not the case with a revenue‐share‐only contract.     

ON‐THE‐JOB SEARCH AND MORAL HAZARD

We analyze the interaction between intertemporal incentive contracts and search frictions associated with on‐the‐job search. In our model, agency problems call for wage contracts with deferred compensation. At the same time workers do on‐the‐job search. Deferred compensation improves workers' incentives to exert effort but distorts their on‐the‐job search decisions. We show that deferred compensation is less attractive when the value to the worker–firm pair of on‐the‐job search is high. Moreover, the interplay between search frictions and wage contracts creates feedback effects. If firms in equilibrium use contracts with deferred compensation, fewer firms with vacancies enter the on‐the‐job search market, and this in turn reduces the distortions created by deferred compensation. These feedback effects between the incentive contracts used and the activity level in the search markets can lead to multiple equilibria: a low‐turnover equilibrium where firms use deferred compensation, and a high‐turnover equilibrium where they do not. Furthermore, the model predicts that firms are more likely to use deferred compensation when search frictions are high and when the gains from on‐the‐job search are small.     

Static and Dynamic Pricing of Excess Capacity in a Make‐to‐Order Environment

We consider a make‐to‐order manufacturer that serves two customer classes: core customers who pay a fixed negotiated price, and “fill‐in” customers who make submittal decisions based on the current price set by the firm. Using a Markovian queueing model, we determine how much the firm can gain by explicitly accounting for the status of its production facility in making pricing decisions. Specifically, we examine three pricing policies: (1) static, state‐independent pricing, (2) constant pricing up to a cutoff state, and (3) general state‐dependent pricing. We determine properties of each policy, and illustrate numerically the financial gains that the firm can achieve by following each policy as compared with simpler policies. Our main result is that constant pricing up to a cutoff state can dramatically outperform a state‐independent policy, while at the same time achieving most of the increase in revenue achievable from general state‐dependent pricing. Thus, we find that constant pricing up to a cutoff state presents an attractive tradeoff between ease of implementation and revenue gain. When the costs of policy design and implementation are taken into account, this simple heuristic may actually out‐perform general state‐dependent pricing in some settings.     

How Costs and Heterogeneous Consumer Price Sensitivity Interact with Add‐On Pricing

Firms often cite cost savings as a reason why they charge separately for add‐ons. Firms also often face situations where consumers' price sensitivity is correlated with their valuation of add‐ons. While cost savings may directly translate into profit gains in some scenarios, this study examines the strategic implications of add‐on pricing and is the first to suggest that cost savings from add‐on pricing may in fact result in profit loss for firms when consumers are heterogeneous in price sensitivity. This is because add‐on pricing can trigger a revenue loss that exceeds any cost savings, thus leading to a negative net profit change for competing firms. Even if firms have the capability to pre‐commit to not adopting add‐on pricing, we show that competing firms can be locked in a prisoner's dilemma where all choose to adopt add‐on pricing and lose profits (as compared to none adopting add‐on pricing). We further show the possibility that the greater the cost of providing the add‐on (and the greater the cost savings generated from add‐on pricing), the worse this profit loss gets.     

Pareto‐Improving Contracts for Express Package Delivery Services

We address the problem of an express package delivery company in structuring a long‐term customer contract whose terms may include prices that differ by day‐of‐week and by speed‐of‐service. The company traditionally offered speed‐of‐service pricing to its customers, but without day‐of‐week differentiation, resulting in customer demands with considerable day‐of‐week seasonality. The package delivery company hoped that using day‐of‐week and speed‐of‐service price differentiation for contract customers would induce these customers to adjust their demands to become counter‐cyclical to the non‐contract demand. Although this usually cannot be achieved by pricing alone, we devise an approach that utilizes day‐of‐week and speed‐of‐service pricing as an element of a Pareto‐improving contract. The contract provides the lowest‐cost arrangement for the package delivery company while ensuring that the customer is at least as well off as he would have been under the existing pricing structure. The contract pricing smoothes the package delivery company's demand and reduces peak requirements for transport capacity. The latter helps to decrease capital costs, which may allow a further price reduction for the customer. We formulate the pricing problem as a biconvex optimization model, and present a methodology for designing the contract and numerical examples that illustrate the achievable savings.     

考虑需求端和产出端不确定性的服务平台定价和商业模式研究

同时考虑平台需求端和供给端不确定性,分析此时服务平台的产品定价、最优激励、商业模式和效率损失传导。通过将委托代理理论和平台理论结合的方法进行分析,研究表明：（1）需求端和产出端的不确定性越高,平台对消费端的定价越低;随着需求端自网络外部性增强,需求端不确定对消费端的影响减弱;随需求端交叉网络外部性增强,需求端不确定对消费端的定价影响增强。（2）需求或产出不确定性越大,分成报酬占总报酬比例越低,平台商业模式更接近于雇佣或自营模式;反之接近纯平台模式。（3）产出端不确定性带来的效率损失在平台两端之间传导。当服务端存在不确定性,服务端报酬和需求端定价同时上升,服务提供端的报酬升高幅度大于需求端产品价格升高幅度。同时,平台将部分效率损失内化,平台承担的效率损失与需求端交叉网络外部性成正比。